The next time you see your physician, consider the times you fill in a paper form. It may seem trivial, but the information could be crucial to making a better diagnosis. Now consider the other forms of healthcare data that permeate your life—and that of your doctor, nurses, and the clinicians working to keep patients thriving. Forms and diagnostic reports are just two examples. The volume of such information is staggering, yet fully utilizing this data is key to reducing healthcare costs, improving patient outcomes, and other healthcare priorities. Now, imagine if artificial intelligence (AI) can be used to help the situation.
The Azure platform offers a wealth of services for partners to enhance, extend, and build industry solutions. Here we describe how SyTrue, a Microsoft partner focusing on healthcare uses Azure to empower healthcare organizations to improve efficiency, reduce costs, and improve patient outcomes.
Billions of records
Valuable insights remain locked in unstructured medical records such as scanned documents in PDF format that, while human-readable, present a major obstacle to the automation and analytics required. Over four billion medical notes are created every year. The clinical and financial insights embodied within these records are needed by an average of
Customers such as Allscripts, Chevron, J.B. Hunt, and thousands of others are migrating their important workloads to Azure where they find unmatched security. While understanding cloud security is initially a concern to many, after digging in, customers often tell us the security posture they can set up within Azure is easier to implement and far more comprehensive than what they can provide for in other environments.
Azure delivers multiple layers of security, from the secure foundation in our physical datacenters, to our operational practices, to engineering processes that follow industry standard Mitre guidelines. On top of that, customers can choose from a variety of self-service security services that work for both Azure and on-premises workloads. We employ more than 3,500 cybersecurity professionals and spend $1 billion annually on security to help protect, detect, and respond to threats – delivering security operations that work 24x7x365 for our customers.
Let’s look at some examples of how Azure delivers unmatched security for your Windows Server and SQL Server workloads.
The broadest built-in protections across hybrid environments with Azure Security Center
Customers can get the broadest built-in protection available across both cloud and on-premises through Azure Security Center. This includes security recommendations for virtual
https://azure.microsoft.com/blog/make-your-data-science-workflow-efficient-and-reproducible-with-mlflow/This blog post was co-authored by Parashar Shah, Senior Program Manager, Applied AI Developer COGS. When data scientists work on building a machine learning model, their experimentation often produces lots of metadata: metrics of models you tested, actual model files, READ MORE
Throughout our Internet of Things (IoT) journey we’ve seen solutions evolve from device-centric models, to spatially-aware solutions that provide real-world context. Last year at Realcomm | IBcon, we announced Azure IoT’s vision for spatial intelligence, diving into scenarios that uniquely join IoT, artificial intelligence (AI), and productivity tools. In the year since, we’ve progressed this vision by introducing new services designed to help enterprise customers across industries optimize the management of their spaces. Across Azure, Dynamics, and Office, Microsoft continues to accelerate results from a growing and diverse set of partners creating smart building solutions on our industry-leading enterprise platform.
This year we’ve returned to Realcomm | IBcon, joined by over 30 partners who have delivered innovative solutions using our spatial intelligence and device security services to provide safety to construction sites, operate buildings more efficiently, utilize space more effectively, and boost occupant productivity and satisfaction. Here we’ll tell you more about a selection of these smart building partners who are accelerating digital transformation in their industries.
IoT is an invaluable part of the smart building lifecycle, even before the building comes to fruition. On construction sites, it’s imperative for companies to prioritize employee safety while ensuring the job
https://azure.microsoft.com/blog/three-things-to-know-about-azure-machine-learning-notebook-vm/Data scientists have a dynamic role. They need environments that are fast and flexible while upholding their organization’s security and compliance policies. Data scientists working on machine learning projects need a flexible environment to run experiments, train models, iterate models, READ MORE
We are excited to announce new capabilities which are apart of time-series forecasting in Azure Machine Learning service. We launched preview of forecasting in December 2018, and we have been excited with the strong customer interest. We listened to our customers and appreciate all the feedback. Your responses helped us reach this milestone. Thank you.
Building forecasts is an integral part of any business, whether it’s revenue, inventory, sales, or customer demand. Building machine learning models is time-consuming and complex with many factors to consider, such as iterating through algorithms, tuning your hyperparameters and feature engineering. These choices multiply with time series data, with additional considerations of trends, seasonality, holidays and effectively splitting training data.
Forecasting within automated machine learning (ML) now includes new capabilities that improve the accuracy and performance of our recommended models:
New forecast function Rolling-origin cross validation Configurable Lags Rolling window aggregate features Holiday detection and featurization Expanded forecast function
We are introducing a new way to retrieve prediction values for the forecast task type. When dealing with time series data, several distinct scenarios arise at prediction time that require more careful consideration. For example, are you able to re-train the model for each forecast?
Azure Cognitive Services provides Text Analytics APIs that simplify extracting information from text data using natural language processing and machine learning. These APIs wrap pre-built language processing capabilities, for example, sentiment analysis, key phrase extraction, entity recognition, and language detection.
Using Text Analytics, businesses can draw deeper insights from interactions with their customers. These insights can be used to create management reports, automate business processes, for competitive analysis, and more. One area that can provide such insights is recorded customer service calls which can provide the necessary data to:
Measure and improve customer satisfaction Track call center and agent performance Look into performance of various service areas
In this blog, we will look at how we can gain insights from these recorded customer calls using Azure Cognitive Services.
Using a combination of these services, such as Text Analytics and Speech APIs, we can extract information from the content of customer and agent conversations. We can then visualize the results and look for trends and patterns.
The sequence is as follows:
Using Azure Speech APIs, we can convert the recorded calls to text. With the text transcriptions in hand, we can then run Text Analytics APIs to gain more insight
During Microsoft Build we announced the preview of the visual interface for Azure Machine Learning service. This new drag-and-drop workflow capability in Azure Machine Learning service simplifies the process of building, testing, and deploying machine learning models for customers who prefer a visual experience to a coding experience. This capability brings the familiarity of what we already provide in our popular Azure Machine Learning Studio with significant improvements to ease the user experience.
The Azure Machine Learning visual interface is designed for simplicity and productivity. The drag-and-drop experience is tailored for:
Data scientists who are more familiar with visual tools than coding. Users who are new to machine learning and want to learn it in an intuitive way. Machine learning experts who are interested in rapid prototyping.
It offers a rich set of modules covering data preparation, feature engineering, training algorithms, and model evaluation. Another great aspect of this new capability is that it is completely web-based with no software installation required. All of this to say, users of all experience levels can now view and work on their data in a more consumable and easy-to-use manner.
One of the biggest challenges data scientists